Publisher's Synopsis
This book covers the practice of running a computer experiment, for which the author is
uniquely qualified: not only has he contributed to the scientific foundations of the topic,
but, as both an academic and a consultant, he has designed and run a wide variety of
computer experiments, in environmental science, physics, and industrial applications.
The main topics for computer experiments are uncertainty propagation (how does
uncertainty in the simulator inputs affect uncertainty in the outputs), sensitivity analysis
(which inputs matter for which outputs), tuning and calibration (learning about the inputs
from measurements on the system), prediction (of the system, possible under a variety of
interventions), and optimization. Throughout the book, the focus is on practical issues, using consistent notation and
definitions, there is substantial practical guidance on running a computer experiment,
including organization and communication. Code snippets, in R, are used to illustrate
algorithms, and the layout of script files.
This practical guide will be essential reading for any scientist or engineer who performs
computer simulations, enabling researchers to develop and perform more robust
experiments more easily and understand how their model functions.
Key Features:
- Written by a practitioner, for other practitioners
- Covers only the useful theory, with focus on practical suggestions
- Many pitfalls illustrated with examples
- Short, self-contained chapters for easy use
- Consistent use of definitions and notation for clarity and communication
